Semi-supervised clustering using multi-assistant-prototypes to represent each cluster

The incorporation of semi-supervision in the cluster detection process has proved especially useful when one wants to get a high consistency between the data partitioning and the knowledge the user has about the data domain. In recent years, several strategies for semi-supervised clustering have been proposed. The approaches adopted by these strategies aim at guiding the process of cluster detection by using constraints: to interfere with the allocation of elements to the most appropriate cluster at each iteration of the algorithm; or to modify the objective function employed. This paper proposes a novel approach for incorporating semi-supervision in the well-known k-means algorithm. This semi-supervised clustering method employs constraint information in the definition of multiple assistant representatives for the centroids used at each iteration of k-means. A refinement process is designed to reduce the number of assistant representatives considered for each centroid without losing the clustering quality. The experimental results with eight synthetic datasets show the potential of the proposed approach for dealing with complex data structures composed by clusters of different shapes.